Due to the absence of a core foundational model, finance teams struggle to provide real-time decision support to the business due to several factors, including:
- Complexity and number of data sources
- Data quality issues
- Difficult and inconsistent data transformation
- Siloed business intelligence instances
- Manual processes
In fact, Teradata research states that over 80% of a finance user’s time is spent on data acquisition and only 20% is spent on true analytics – all due to the lack of a trusted source for finance focused data. Accenture’s “The CFO Reimagined” research states “76% of CFOs agree that without ‘one version of the truth’ across business units, their organization will struggle to meet its objectives.” These challenges must be addressed to enable a “digital ready” finance function and it all begins with creating the core finance foundation.
Key Functional Issues – Common Problems
A digital financial ecosystem is the driver for solving many of the issues facing the CFO office today, including:
- Manual repeat of financial data acquisition
- Data quality
- Lack of business-friendly tools for analytics, reporting and visualization
- Repetition of similar projects due to data silos
- Difficulty in providing analytics on a timely basis
- Market scarcity of accounting resources
- Increasingly complex regulatory/compliance requirements
- Complexity associated with automation of core finance processes
To solve these common data and analytical challenges, the CFO needs to become “The Informational Broker” and address three key issues:
- Provide informational transparency
- Provide relevant, timely and accurate strategic and financial decisions
- Provide access across the entire enterprise
A core finance foundational model provides users with the information and tools they need to analyze the data, at the proper level of detail. This enables users to understand the causal effects of reported results. With this information, users have the answers that are timely, relevant and trusted and can provide benefits to everyone across the business, including management, operations, marketing, customer experience and human resources. In fact, this process results in potentially thousands of new users of data, reports, dashboards and analytics provided by the CFO office.
A trusted core finance foundation empowers leaders to provide new business value and digitally driven analytics that focus on predictive and prescriptive insights, while continuing to provide traditional, legacy and descriptive analytics.
CFO Analytics Framework
At Teradata, our CFO Analytics Framework enables users to identify the key design principles required for analytics and is comprised of:
1. Integrated Financial Insights integrate the general ledger and sub-ledger data to provide detailed insights on accounting and finance focused data. This requires master and reference data management, a finance focused logical data model and the ability to integrate across multiple ledgers/ERP platforms and creates a trusted single view of finance or “golden record” of financial data. As a result, accounting/financial analysts deliver business outcome driven analytics, including CFO KPI Dashboards, Procure to Pay, Order to Cash, Indirect and Direct Spend Analytics. A strong data platform enables greater level of detail that promotes drill down and drill up capabilities. In addition, storing the results in-database enables the socialization of the results with a wider audience as well as downstream models and applications.
2. Super Ledger integrates operational and transactional data into the financial data platform. This enables better traceability from source to report, and drives dimensional views of financial reconciliations, operational reporting, customer segmentation, products, and revenue assurance. Combined with Integrated Financial Insights, Super Ledger provides the basis for detailed dimensional analytics and reporting.
3. Multi-dimensional Profitability (MDP) creates the ability to model and allocate financial components down to the lowest level of detail. MDP includes operating revenue (product / non-product revenues, rebates, reimbursements, etc.), direct / indirect expense, unit costing, risk components, capital costs and other profitability attributes, and typically leverages advanced financial modeling tools. The analytical data set produced yields new insights into profitability at any dimensional level (customer, product, location, channel, etc.). One model, many uses. The results also feed downstream models and applications like price optimization, customer segmentation, customer lifetime value and product rationalization models.
4. Advanced Finance Modeling provides the ability to leverage data from Integrated Financial Insights, Super Ledger and MDP to create a finance driven analytical ecosystem. In addition to descriptive analytics common with finance analytics, the CFO data foundation enables the use of advanced predictive and prescriptive modeling tools, artificial intelligence and machine / deep learning models that provide more value-added analytical results especially in the areas of audit automation, compliance and financial forecasting.
The data model is the key driver in this process and integrates the data from multiple platforms into the golden record. It also plays a key role in integrating transactional and other non-financial data that might be required for more sophisticated analytics.
Just like any other construction project, a solid foundation is needed before building an analytical house! The core finance foundation, supported by tools (data model, MDM, RDM, modelling tools, etc.), creates a trusted, auditable and traceable source of all things financial. With these tools, finance teams provide more strategic insights to the business on a timelier and more accurate basis.
In the next CFO Analytics blog, we will dive into how we can automate the CFO analytics process to create repeatable analytics and deliver results faster.